Support Vector Machines Used to Estimate the Battery State of Charge
Open accessAbstract
The aim of this study is to estimate the state of charge (SOC) of a high-capacity lithium iron manganese phosphate (LiFeMnPO$_{4}$) battery cell from an experimental dataset using a support vector machine (SVM) approach. SVM is a type of learning machine based on statistical learning theory. Many applications require accurate measurement of battery SOC in order to give users an indication of available runtime. It is particularly important for electric vehicles or portable devices. In this paper, the proposed SOC estimator extracts model parameters from battery charging/discharging testing cycles, using cell current, cell voltage, and cell temperature as independent variables. Tests are carried out on a 60 Ah lithium-ion cell with the dynamic stress test cycle to set up the SVM model. The SVM SOC estimator maintains a high level of accuracy, better than 6% over all ranges of operation, whether the battery is charged/discharged at constant current or it is operating in a variable current profile.
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SVM can be applied to classify battery states from voltage, current, and temperature inputs for SOC estimation.
“By regarding state of charge SOC estimation, SVM can be applied in the classification of battery states from voltage current and temperature inputs [52].”
From Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement (2024) · cites this paper as [52]
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- Authors
- Álvarez Antón, J. C., García Nieto, P. J., Blanco Viejo, C., & Vilán Vilán, J. A.
- Affiliations
- Department of Electrical Engineering , University of Oviedo, Gijón, Spain
- Department of Mathematics, University of Oviedo, 33007 Oviedo, Spain
- Department of Electrical Engineering , University of Oviedo, 33204 Gijón, Spain
- Department of Mechanical Engineering , University of Vigo, 36310 Vigo, Spain
- Venue
- IEEE Transactions on Power Electronics
- Published
- 2013-12
- Volume
- 28
- Issue
- 12
- Pages
- 5919-5926
- ISSN
- 0885-8993 (ISSN-L)
- 1941-0107
- OpenAlex
- W1969506110
- Cited by
- 512
- References
- 0
- Citations by year
- 2026: 36 · 2025: 57 · 2024: 59 · 2023: 61 · 2022: 69 · 2021: 45 · 2020: 41 · 2019: 39 · 2018: 21 · 2017: 25 · 2016: 16 · 2015: 25 · 2014: 16 · 2013: 2
- FWCI
- 30.62
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- IEEE Transactions on Power Electronics (publishedVersion)
- Consultation of the Doctoral Thesis Database (TESEO) (Ministerio de Educación, Cultura y Deporte) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Keywords
- state of charge estimation, support vector machine, lithium-ion cells, dynamic stress test, electric vehicles, portable devices
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